compare bilateral

doc/examples/plot_16bitbilateral.py

@@ -1,9 +1,23 @@
 """
 ==============================
-Simplified bilateral filtering
+Bilateral mean
 ==============================
+This example compares
 
-to complete
+* local mean
+* percentile mean
+* bilateral mean
+
+build on the local histogram distribution
+local mean uses all pixels belonging to the structuring element to compute average gray level,
+percentile mean uses only values between percentiles p0 and p1 (here 10% and 90%),
+whereas bilateral mean uses only pixels of the structuring element having a gray level situated inside
+g-s0 and g+s1 (here g-500 and g+500).
+The filters are applied on a 16 bit image (actual bitdepth is 12bit).
+
+Percentile and usual mean give here similar results, these filters smooth the complete image (background and details).
+Bilateral mean exhibits a high filtering rate for continuous area (i.e. background) while image higher frequencies
+remains untouched.
 
 """
 import numpy as np
@@ -13,23 +27,21 @@ from skimage import data
 from skimage.morphology import disk
 import skimage.filter.rank as rank
 
-a8 = (data.coins()).astype('uint8')
-
 a16 = (data.coins()).astype('uint16')*16
-selem = np.ones((20,20),dtype='uint8')
-f1 = rank.percentile_mean(a8,selem = selem,p0=.1,p1=.9)
+selem = disk(20)
+
+f1 = rank.percentile_mean(a16,selem = selem,p0=.1,p1=.9)
 f2 = rank.bilateral_mean(a16,selem = selem,s0=500,s1=500)
-selem = disk(50)
-f3 = rank.equalize(a16,selem = selem)
+f3 = rank.mean(a16,selem = selem)
 
 # display results
-fig, axes = plt.subplots(nrows=3, figsize=(15,15))
+fig, axes = plt.subplots(nrows=3, figsize=(15,10))
 ax0, ax1, ax2 = axes
 
-ax0.imshow(np.hstack((a8,f1)))
+ax0.imshow(np.hstack((a16,f1)))
 ax0.set_title('percentile mean')
 ax1.imshow(np.hstack((a16,f2)))
 ax1.set_title('bilateral mean')
 ax2.imshow(np.hstack((a16,f3)))
-ax2.set_title('local equalization')
+ax2.set_title('local mean')
 plt.show()